July 2026Unreviewed
Salience Induction against Multi-Hop RAG Agents: Threat and Defense
Xingfu Zhou, Pengfei Wang, Yuan Zhou, Wei Xie, Xu Zhou
Abstract
Agentic retrieval-augmented generation (RAG) systems increasingly retrieve external evidence and orchestrate tools for knowledge-intensive applications. In Multi-Hop question answering, agents chain facts across documents. Existing defenses focus on content poisoning, which injects false facts, and prompt injection, which embeds directives. We identify a third attack surface: the salience channel, through which fact position, emphasis, framing, and semantic proximity can redirect reasoning even
Categories
Framework mappings
OWASP Top 10 for LLM Applications
- LLM01Prompt Injection
- LLM04Data and Model Poisoning
MITRE ATLAS
- AML.T0020Poison Training Data
- AML.T0051LLM Prompt Injection
Suggested from the entry's categories.
Cite
@misc{zhou2026salience,
title = {{Salience Induction against Multi-Hop RAG Agents: Threat and Defense}},
author = {Xingfu Zhou and Pengfei Wang and Yuan Zhou and Wei Xie and Xu Zhou},
year = {2026},
month = jul,
eprint = {2607.17535},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2607.17535}
}